They have complementary information to DXA and are potentially im

They have complementary information to DXA and are potentially important for the assessment of femoral bone strength,

even though they are not an integral whole-bone tool such as the finite element method [38–42]. DXA parameters had the highest correlations with FL in the neck ROI and the total ROI, similar to previous MK-2206 solubility dmso studies [32, 33]. In contrast, trabecular structure parameters achieved the lowest correlations with FL and adjusted FL parameters mostly in the neck and the highest correlations by the majority in the femoral head. A direct comparison of DXA and trabecular structure parameters of the head was not possible, since DXA parameters were not measured in the femoral head due to the superimposition

with the acetabulum in in vivo examination conditions. To the Thiazovivin best of our knowledge, we applied for the first time an automated 3D segmentation algorithm on CT images of the proximal femur for trabecular bone structure analysis. This algorithm has already been used for trabecular BMD analysis [24]. Several automated VOI-fitting algorithms have been described for trabecular BMD analysis [6, 43], but none for trabecular bone structure analysis. Saparin et al. applied an automated 2D ROI placement on CT images of the femoral head and neck [44]. However, a 3D-based algorithm is essential to calculate 3D fuzzy logic,

SIM, and MF and thus is advantageous. A limiting factor of the algorithm was the manual corrections of segmentation in 14 cases (7.5% of all specimens). These corrections can induce operator-dependent Rutecarpine errors, but the determined reproducibility errors for segmentation indicated a good reproducibility of the morphometric parameters aside from app.TbSp in the neck. Reproducibility errors for segmentation and segmentation with repositioning were highest in the femur neck. Due to strong inhomogeneous bone structure in the femur neck, minor variations of the VOI position can induce major differences of the parameter values. Bauer et al. selected ROIs manually and reported highest reproducibility errors of the morphometric parameters also in the femur neck [13]. Reproducibility errors were considerably lower with our automated algorithm. They amounted to 0.11% to 9.41% for segmentation, compared to 1.8% to 31.3% using the manual technique of Bauer et al. This automated algorithm MLN2238 affords lower operator-dependent errors and additionally an enormous saving in time. The calculation of the trabecular bone structure parameters has limitations. Images have to be binarized to compute the morphometric parameters and MF. Standardization was achieved by using the reference phantom, but the results are strongly dependent on the chosen threshold.

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